{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "392e88c6",
   "metadata": {},
   "source": [
    "# Model Output Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1388915",
   "metadata": {},
   "source": [
    "### Read Me"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a46350ff",
   "metadata": {},
   "source": [
    "This notebook vizualizes model outputs for supervised neural methods from the rPPG Toolbox. To use this notebook follow the following steps:\n",
    "\n",
    "* STEP 1: Specify the `TEST.OUTPUT_SAVE_DIR` field in the `.yaml` config file of a neural method experiment.\n",
    "* STEP 2: Run the neural method experiment. A `.pickle` file containing the test-data predictions and labels will be created in `TEST.OUTPUT_SAVE_DIR`.\n",
    "* STEP 3: Add the path to the `pickle` file in the `TODO: Variable to Set` section as `data_out_path`.\n",
    "* STEP 4: Add values for `trial_idx` (a value between 0 and the `Num Trials` print in the previous cell), `chunk_size` (number of samples to plot out), `chunk_num` (the chunk of size `chunk_size` in the signal). We suggest using the defaults at first.\n",
    "* STEP 5: Run all cells. The predicted output, plotted against the ground truth ppg waveform, will be plotted in the final cell"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e8715af",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "81dc2cd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scipy in /opt/anaconda3/lib/python3.8/site-packages (1.7.3)\r\n",
      "Requirement already satisfied: numpy<1.23.0,>=1.16.5 in /opt/anaconda3/lib/python3.8/site-packages (from scipy) (1.20.3)\r\n"
     ]
    }
   ],
   "source": [
    "# !pip install natsort\n",
    "!pip install scipy\n",
    "# !pip install natsort\n",
    "# !pip install ipywidgets\n",
    "\n",
    "import cv2\n",
    "import pickle\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "\n",
    "import torch\n",
    "\n",
    "import numpy as np\n",
    "# import ipywidgets as widgets\n",
    "from IPython.display import display, clear_output\n",
    "from natsort import natsorted\n",
    "import os\n",
    "import scipy\n",
    "from scipy.sparse import spdiags\n",
    "from scipy.signal import butter\n",
    "import math\n",
    "from scipy import linalg\n",
    "from scipy import signal\n",
    "from scipy import sparse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ed1f270",
   "metadata": {},
   "source": [
    "### Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d99b8717",
   "metadata": {},
   "outputs": [],
   "source": [
    "# HELPER FUNCTIONS\n",
    "\n",
    "def _reform_data_from_dict(data, flatten=True):\n",
    "    \"\"\"Helper func for calculate metrics: reformat predictions and labels from dicts. \"\"\"\n",
    "    sort_data = sorted(data.items(), key=lambda x: x[0])\n",
    "    sort_data = [i[1] for i in sort_data]\n",
    "    sort_data = torch.cat(sort_data, dim=0)\n",
    "\n",
    "    if flatten:\n",
    "        sort_data = np.reshape(sort_data.cpu(), (-1))\n",
    "    else:\n",
    "        sort_data = np.array(sort_data.cpu())\n",
    "\n",
    "    return sort_data\n",
    "\n",
    "def _process_signal(signal, fs=30, diff_flag=True):\n",
    "    # Detrend and filter\n",
    "    use_bandpass = True\n",
    "    if diff_flag:  # if the predictions and labels are 1st derivative of PPG signal.\n",
    "        gt_bvp = _detrend(np.cumsum(signal), 100)\n",
    "    else:\n",
    "        gt_bvp = _detrend(signal, 100)\n",
    "    if use_bandpass:\n",
    "        # bandpass filter between [0.75, 2.5] Hz\n",
    "        # equals [45, 150] beats per min\n",
    "        [b, a] = butter(1, [0.75 / fs * 2, 2.5 / fs * 2], btype='bandpass')\n",
    "        signal = scipy.signal.filtfilt(b, a, np.double(signal))\n",
    "    return signal\n",
    "\n",
    "def _detrend(input_signal, lambda_value):\n",
    "    \"\"\"Detrend PPG signal.\"\"\"\n",
    "    signal_length = input_signal.shape[0]\n",
    "    # observation matrix\n",
    "    H = np.identity(signal_length)\n",
    "    ones = np.ones(signal_length)\n",
    "    minus_twos = -2 * np.ones(signal_length)\n",
    "    diags_data = np.array([ones, minus_twos, ones])\n",
    "    diags_index = np.array([0, 1, 2])\n",
    "    D = spdiags(diags_data, diags_index,\n",
    "                (signal_length - 2), signal_length).toarray()\n",
    "    detrended_signal = np.dot(\n",
    "        (H - np.linalg.inv(H + (lambda_value ** 2) * np.dot(D.T, D))), input_signal)\n",
    "    return detrended_signal\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cac8bf60",
   "metadata": {},
   "source": [
    "### TODO: Variables To Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "59381e72",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_out_path = \"./sample_output.pickle\"  # Output Data Path \n",
    "trial_idx = 0\n",
    "chunk_size = 180 # size of chunk to visualize: -1 will plot the entire signal\n",
    "chunk_num = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19330e88",
   "metadata": {},
   "source": [
    "### Read Data, and Extract Trials List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bfec0ca1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num Trials 42\n"
     ]
    }
   ],
   "source": [
    "# Read in data and list subjects\n",
    "with open(data_out_path, 'rb') as f:\n",
    "    data = pickle.load(f)\n",
    "    \n",
    "# List of all video trials\n",
    "trial_list = list(data['predictions'].keys())\n",
    "print('Num Trials', len(trial_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ee86751a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reform label and prediction vectors from multiple trial chunks\n",
    "prediction = np.array(_reform_data_from_dict(data['predictions'][trial_list[trial_idx]]))\n",
    "label = np.array(_reform_data_from_dict(data['labels'][trial_list[trial_idx]]))\n",
    "\n",
    "# Read in meta-data from pickle file\n",
    "fs = data['fs'] # Video Frame Rate\n",
    "label_type = data['label_type'] # PPG Signal Transformation: `DiffNormalized` or `Standardized`\n",
    "diff_flag = (label_type == 'DiffNormalized')\n",
    "\n",
    "if chunk_size == -1:\n",
    "    chunk_size = len(prediction)\n",
    "    chunk_num = 0\n",
    "\n",
    "# Process label and prediction signals\n",
    "prediction = _process_signal(prediction, fs, diff_flag=diff_flag)\n",
    "label = _process_signal(label, fs, diff_flag=diff_flag)\n",
    "start = (chunk_num)*chunk_size\n",
    "stop = (chunk_num+1)*chunk_size\n",
    "samples = stop - start\n",
    "x_time = np.linspace(0, samples/fs, num=samples)\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(x_time, prediction[start:stop], color='r')\n",
    "plt.plot(x_time, label[start:stop], color='black')\n",
    "plt.title('Trial: ' + trial_list[trial_idx])\n",
    "plt.legend(['Predictions', 'Labels'])\n",
    "plt.xlabel('Time (s)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4376c9c8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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